The introduction of the Electronic Medical Record (EMR) has improved convenience in accessing and organizing medical reports. With the increasing demand for biomedical tools based on deep learning, obtaining large volumes of labeled data is essential for training an effective model. One major category where such deep learning models excel is in the area of computer assisted diagnosis (CADx), and several works (e.g. [1]}, [2]}, [3]}) have demonstrated effective utilization of weakly labeled data to achieve promising performance. Since understanding medical data requires specialized training, datasets often contain a small subset of all past exams, that are manually relabeled by doctors for the target task. Not only is this a labour-intensive process, but the resulting dataset is often too small to represent the true distribution, resulting in underperforming models.
